class_docstr: Beam search decoder for RNN-T model with biasing support.
  
  Args:
      model (RNNT): RNN-T model to use.
      blank (int): index of blank token in vocabulary.
      temperature (float, optional): temperature to apply to joint network output.
          Larger values yield more uniform samples. (Default: 1.0)
      hypo_sort_key (Callable[[Hypothesis], float] or None, optional): callable that computes a score
          for a given hypothesis to rank hypotheses by. If ``None``, defaults to callable that returns
          hypothesis score normalized by token sequence length. (Default: None)
      step_max_tokens (int, optional): maximum number of tokens to emit per input time step. (Default: 100)
      trie (list, optional): the prefix tree for TCPGen biasing
      biasing (bool, optional): If true, do biasing, otherwise use standard RNN-T support
code: def _gen_new_hypos(
      self,
      base_hypos: List[Hypothesis],
      tokens: List[int],
      scores: List[float],
      t: int,
      device: torch.device,
  ) -> List[Hypothesis]:
      tgt_tokens = torch.tensor(
          [[token] for token in tokens], device=device
      )
      states = _batch_state(base_hypos)
      pred_out, _, pred_states = self.model.predict(
          tgt_tokens,
          torch.tensor([1] * len(base_hypos), device=device),
          states,
      )
      new_hypos: List[Hypothesis] = []
      for i, h_a in enumerate(base_hypos):
          new_tokens = _get_hypo_tokens(h_a) + [tokens[i]]
          if self.dobiasing:
              new_trie = self.model.get_tcpgen_step(
                  tokens[i], _get_hypo_trie(h_a), self.resettrie
              )
          else:
              new_trie = self.resettrie
          new_hypos.append(
              (
                  new_tokens,
                  pred_out[i].detach(),
                  _slice_state(pred_states, i, device),
                  scores[i],
                  new_trie,
              )
          )
      return new_hypos
  
imports: from typing import Callable, Dict, List, Optional, Tuple
  import torch
  from torchaudio.models import RNNT
  from torchaudio.prototype.models.rnnt import TrieNode
label_desc: Use image node_id 8 for calling the RNNTBeamSearchBiasing obj's underlying member method code with example usage: obj._gen_new_hypos(base_hypos, tokens, scores, t, device) and returns: new_hypos